{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "def load_housing_data(housing_path = './'):   # 加载数据，路径整合\n",
    "    csv_path = os.path.join(housing_path, 'housing.csv')\n",
    "    return pd.read_csv(csv_path)     # DataFrame 类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  float64\n",
      " 3   total_rooms         20640 non-null  float64\n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  float64\n",
      " 6   households          20640 non-null  float64\n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  float64\n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()  # 查看数据类型，个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts() # object类型，查看其不同值个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe() # 查看各属性值分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline   # 直方图查看各属性整体分布\n",
    "import matplotlib.pyplot as plt\n",
    "housing.hist(bins=50,figsize=(20,15))  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房龄和房价设置了上限\n",
    "    1.对上限数重新采集\n",
    "    2.上限数据移除\n",
    "    \n",
    "### 重尾 头高尾长\n",
    "    *转换数据（正态分布）\n",
    "###    收入特征\n",
    "    *收入数据如何缩放？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集和测试集\n",
    "    ·训练集用于模型训练 占整个数据集的80%，测试集占20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data,test_radio):\n",
    "    np.random.seed(23) #给随机种子，确定每次一样，方便训练\n",
    "    indices = np.random.permutation(len(data)) # 对原数据集进行重新洗牌，随机打乱原来元素顺序\n",
    "    test_set_size = int(len(data)*test_radio)  \n",
    "    test_indices = indices[:test_set_size]      \n",
    "    train_indices = indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test(housing,0.2)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对样本设置唯一的标识符，对标识符hash值，去hash的最后一个字节，值小于51,256*20%，放入测试集\n",
    "# hash值相同，对象不一定相同，hash不同，对象一定不同\n",
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier, test_radio, hash = hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1] < 256*test_radio # 返回摘要，作为二进制数据字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data,test_radio, id_column):       # 使用id 行索引作为标识进行拆分\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_, test_radio))  \n",
    "    return data.loc[-in_test_set], data.loc[in_test_set]    # 依据哈希值取数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13643</td>\n",
       "      <td>-117.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>48.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>2.5673</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11573</td>\n",
       "      <td>-118.00</td>\n",
       "      <td>33.77</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>3.4327</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20322</td>\n",
       "      <td>-119.14</td>\n",
       "      <td>34.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.5714</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16484</td>\n",
       "      <td>-121.06</td>\n",
       "      <td>38.25</td>\n",
       "      <td>13.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.6528</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12136</td>\n",
       "      <td>-117.13</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0  13643    -117.33     34.06                48.0        732.0   \n",
       "1  11573    -118.00     33.77                24.0       1324.0   \n",
       "2  20322    -119.14     34.23                 8.0        243.0   \n",
       "3  16484    -121.06     38.25                13.0        651.0   \n",
       "4  12136    -117.13     33.89                 4.0       1611.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income ocean_proximity  \\\n",
       "0           149.0       486.0       139.0         2.5673          INLAND   \n",
       "1           267.0       687.0       264.0         3.4327       <1H OCEAN   \n",
       "2            75.0       102.0        80.0         2.5714      NEAR OCEAN   \n",
       "3           102.0       301.0       104.0         3.6528          INLAND   \n",
       "4           239.0       275.0        84.0         3.5781          INLAND   \n",
       "\n",
       "  income_cat  \n",
       "0          2  \n",
       "1          3  \n",
       "2          2  \n",
       "3          3  \n",
       "4          3  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id = housing.reset_index()  # 使用行索引作为标识符\n",
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
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       "      <th>households</th>\n",
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       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
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       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "6      6    -122.25     37.84                52.0       2535.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "6           489.0      1094.0       514.0         3.6591            299200.0   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id,0.2,'index')\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    基于行索引，不能删除和中间插入数据，只能末尾插入，否则行索引会变\n",
    "    寻找稳定特征来创建唯一标识符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
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       "      <td>-122.23</td>\n",
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       "      <td>880.0</td>\n",
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       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
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       "      <td>1106.0</td>\n",
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       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id['id'] = housing['longitude'] * 1000 + housing['latitude']  # 经纬度组合作为索引\n",
    "housing_with_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>219.0</td>\n",
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       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
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       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                41.0        880.0   \n",
       "1      1    -122.22     37.86                21.0       7099.0   \n",
       "2      2    -122.24     37.85                52.0       1467.0   \n",
       "3      3    -122.25     37.85                52.0       1274.0   \n",
       "4      4    -122.25     37.85                52.0       1627.0   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0           129.0       322.0       126.0         8.3252            452600.0   \n",
       "1          1106.0      2401.0      1138.0         8.3014            358500.0   \n",
       "2           190.0       496.0       177.0         7.2574            352100.0   \n",
       "3           235.0       558.0       219.0         5.6431            341300.0   \n",
       "4           280.0       565.0       259.0         3.8462            342200.0   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, 'id')\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>-121.97</td>\n",
       "      <td>37.93</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3241.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>6.6134</td>\n",
       "      <td>307000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14652</th>\n",
       "      <td>-117.15</td>\n",
       "      <td>32.80</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1413.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>2.3578</td>\n",
       "      <td>166700.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3548</th>\n",
       "      <td>-118.61</td>\n",
       "      <td>34.25</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8295.0</td>\n",
       "      <td>1506.0</td>\n",
       "      <td>3903.0</td>\n",
       "      <td>1451.0</td>\n",
       "      <td>5.5111</td>\n",
       "      <td>276600.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6730</th>\n",
       "      <td>-118.14</td>\n",
       "      <td>34.11</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2742.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>1153.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>8.1124</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18445</th>\n",
       "      <td>-121.81</td>\n",
       "      <td>37.25</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4096.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>2128.0</td>\n",
       "      <td>618.0</td>\n",
       "      <td>6.2957</td>\n",
       "      <td>251800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "1652     -121.97     37.93                 4.0       3241.0           464.0   \n",
       "14652    -117.15     32.80                41.0       1413.0           261.0   \n",
       "3548     -118.61     34.25                16.0       8295.0          1506.0   \n",
       "6730     -118.14     34.11                52.0       2742.0           422.0   \n",
       "18445    -121.81     37.25                25.0       4096.0           623.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "1652       1552.0       494.0         6.6134            307000.0   \n",
       "14652      1070.0       259.0         2.3578            166700.0   \n",
       "3548       3903.0      1451.0         5.5111            276600.0   \n",
       "6730       1153.0       414.0         8.1124            500001.0   \n",
       "18445      2128.0       618.0         6.2957            251800.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "1652           INLAND  \n",
       "14652      NEAR OCEAN  \n",
       "3548        <1H OCEAN  \n",
       "6730        <1H OCEAN  \n",
       "18445       <1H OCEAN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_sete = train_test_split(housing,test_size = 0.2, random_state = 12)\n",
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x228a4afbb88>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 希望测试集能够代表数据集中各不同类型收入\n",
    "    1.创建收入类别属性\n",
    "    2.数据分层不应过多，但每层应该有足够的数据量\n",
    "    3.将收入中位数除以1.5，限制收入类别数量，使用ceil取整，得到离散类别，将大于5的类别合并为类别5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5.0\n",
       "1    5.0\n",
       "2    5.0\n",
       "3    4.0\n",
       "4    3.0\n",
       "5    3.0\n",
       "6    3.0\n",
       "7    3.0\n",
       "8    2.0\n",
       "9    3.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "# housing['income_cat'].head(10)\n",
    "housing['income_cat'].where(housing['income_cat']< 5, 5.0, inplace = True)\n",
    "housing['income_cat'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    5\n",
       "2    5\n",
       "3    4\n",
       "4    3\n",
       "5    3\n",
       "6    3\n",
       "7    3\n",
       "8    2\n",
       "9    3\n",
       "Name: income_cat, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'] = pd.cut(housing['median_income'],bins=[0., 1.5, 3.0, 4.5, 6., np.inf], labels= [1, 2, 3, 4, 5]) # 把连续值转化为标签\n",
    "housing['income_cat'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    7236\n",
       "2    6581\n",
       "4    3639\n",
       "5    2362\n",
       "1     822\n",
       "Name: income_cat, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x228a23c9348>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据收入类别进行分层采样\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state= 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index, test_index in split.split(housing, housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13643 11573 20322 ...  6537  2227 10725]\n"
     ]
    }
   ],
   "source": [
    "for  train_index, test_index in split.split(housing, housing['income_cat']):\n",
    "    print(train_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13643</th>\n",
       "      <td>-117.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>48.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>2.5673</td>\n",
       "      <td>68200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11573</th>\n",
       "      <td>-118.00</td>\n",
       "      <td>33.77</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>3.4327</td>\n",
       "      <td>192800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20322</th>\n",
       "      <td>-119.14</td>\n",
       "      <td>34.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.5714</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16484</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>38.25</td>\n",
       "      <td>13.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.6528</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12136</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>244400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6388</th>\n",
       "      <td>-118.03</td>\n",
       "      <td>34.16</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>7.1615</td>\n",
       "      <td>484700.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11197</th>\n",
       "      <td>-117.93</td>\n",
       "      <td>33.84</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>3.4750</td>\n",
       "      <td>282500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20206</th>\n",
       "      <td>-119.22</td>\n",
       "      <td>34.27</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>5.0679</td>\n",
       "      <td>234300.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>-121.92</td>\n",
       "      <td>37.96</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5332.0</td>\n",
       "      <td>884.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>839.0</td>\n",
       "      <td>5.2798</td>\n",
       "      <td>237400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10829</th>\n",
       "      <td>-117.94</td>\n",
       "      <td>33.67</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2552.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>925.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>8.1839</td>\n",
       "      <td>367000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.98</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>6.0289</td>\n",
       "      <td>218600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14860</th>\n",
       "      <td>-117.08</td>\n",
       "      <td>32.64</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1651.0</td>\n",
       "      <td>533.0</td>\n",
       "      <td>947.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>1.6806</td>\n",
       "      <td>141700.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5721</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>34.18</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1853.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>757.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>3.6875</td>\n",
       "      <td>422000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13304</th>\n",
       "      <td>-117.63</td>\n",
       "      <td>34.09</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3490.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>2818.0</td>\n",
       "      <td>688.0</td>\n",
       "      <td>2.8977</td>\n",
       "      <td>126200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8984</th>\n",
       "      <td>-118.44</td>\n",
       "      <td>33.99</td>\n",
       "      <td>44.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>5.9641</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18972</th>\n",
       "      <td>-122.00</td>\n",
       "      <td>38.23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2062.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>872.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>5.2636</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16492</th>\n",
       "      <td>-121.11</td>\n",
       "      <td>38.04</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1083.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.9241</td>\n",
       "      <td>187500.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13297</th>\n",
       "      <td>-117.65</td>\n",
       "      <td>34.08</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1609.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>5.4689</td>\n",
       "      <td>158200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7978</th>\n",
       "      <td>-118.17</td>\n",
       "      <td>33.86</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1701.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>3.0250</td>\n",
       "      <td>162300.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.79</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1372.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>1237.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>95500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "13643    -117.33     34.06                48.0        732.0           149.0   \n",
       "11573    -118.00     33.77                24.0       1324.0           267.0   \n",
       "20322    -119.14     34.23                 8.0        243.0            75.0   \n",
       "16484    -121.06     38.25                13.0        651.0           102.0   \n",
       "12136    -117.13     33.89                 4.0       1611.0           239.0   \n",
       "6388     -118.03     34.16                36.0       1401.0           218.0   \n",
       "11197    -117.93     33.84                26.0       2811.0           612.0   \n",
       "20206    -119.22     34.27                30.0       1937.0           295.0   \n",
       "1657     -121.92     37.96                14.0       5332.0           884.0   \n",
       "10829    -117.94     33.67                26.0       2552.0           314.0   \n",
       "1688     -122.27     37.98                23.0       3455.0           479.0   \n",
       "14860    -117.08     32.64                11.0       1651.0           533.0   \n",
       "5721     -118.23     34.18                47.0       1853.0           345.0   \n",
       "13304    -117.63     34.09                19.0       3490.0           816.0   \n",
       "8984     -118.44     33.99                44.0        305.0            72.0   \n",
       "18972    -122.00     38.23                 1.0       2062.0           343.0   \n",
       "16492    -121.11     38.04                32.0       1083.0           188.0   \n",
       "13297    -117.65     34.08                40.0       1609.0           258.0   \n",
       "7978     -118.17     33.86                44.0       1701.0           396.0   \n",
       "189      -122.24     37.79                47.0       1372.0           395.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "13643       486.0       139.0         2.5673             68200.0   \n",
       "11573       687.0       264.0         3.4327            192800.0   \n",
       "20322       102.0        80.0         2.5714            500001.0   \n",
       "16484       301.0       104.0         3.6528            200000.0   \n",
       "12136       275.0        84.0         3.5781            244400.0   \n",
       "6388        667.0       225.0         7.1615            484700.0   \n",
       "11197      1374.0       566.0         3.4750            282500.0   \n",
       "20206       695.0       313.0         5.0679            234300.0   \n",
       "1657       2093.0       839.0         5.2798            237400.0   \n",
       "10829       925.0       323.0         8.1839            367000.0   \n",
       "1688       1375.0       474.0         6.0289            218600.0   \n",
       "14860       947.0       515.0         1.6806            141700.0   \n",
       "5721        757.0       310.0         3.6875            422000.0   \n",
       "13304      2818.0       688.0         2.8977            126200.0   \n",
       "8984        156.0        70.0         5.9641            275000.0   \n",
       "18972       872.0       268.0         5.2636            191300.0   \n",
       "16492       471.0       178.0         2.9241            187500.0   \n",
       "13297       624.0       242.0         5.4689            158200.0   \n",
       "7978       1091.0       384.0         3.0250            162300.0   \n",
       "189        1237.0       303.0         2.1250             95500.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "13643          INLAND          2  \n",
       "11573       <1H OCEAN          3  \n",
       "20322      NEAR OCEAN          2  \n",
       "16484          INLAND          3  \n",
       "12136          INLAND          3  \n",
       "6388           INLAND          5  \n",
       "11197       <1H OCEAN          3  \n",
       "20206      NEAR OCEAN          4  \n",
       "1657           INLAND          4  \n",
       "10829       <1H OCEAN          5  \n",
       "1688         NEAR BAY          5  \n",
       "14860      NEAR OCEAN          2  \n",
       "5721        <1H OCEAN          3  \n",
       "13304          INLAND          2  \n",
       "8984        <1H OCEAN          4  \n",
       "18972          INLAND          4  \n",
       "16492          INLAND          2  \n",
       "13297          INLAND          4  \n",
       "7978        <1H OCEAN          3  \n",
       "189          NEAR BAY          2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350533\n",
       "2    0.318798\n",
       "4    0.176357\n",
       "5    0.114583\n",
       "1    0.039729\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有住房数据根据收入类别比例分布，收入占类别百分比\n",
    "strat_test_set['income_cat'].value_counts()/len(strat_test_set) # 分层抽样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0.350581\n",
       "2    0.318847\n",
       "4    0.176308\n",
       "5    0.114438\n",
       "1    0.039826\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].value_counts()/len(housing)  # 完整数据集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_cat_proportions(data):\n",
    "    return data[\"income_cat\"].value_counts() / len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "\n",
    "compare_props = pd.DataFrame({\n",
    "    \"全部数据\": income_cat_proportions(housing),\n",
    "    \"分层抽样\": income_cat_proportions(strat_test_set),\n",
    "    \"随机抽样\": income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"随机. %error\"] = 100 * compare_props[\"随机抽样\"] / compare_props[\"全部数据\"] - 100\n",
    "compare_props[\"分层. %error\"] = 100 * compare_props[\"分层抽样\"] / compare_props[\"全部数据\"] - 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>全部数据</th>\n",
       "      <th>分层抽样</th>\n",
       "      <th>随机抽样</th>\n",
       "      <th>随机. %error</th>\n",
       "      <th>分层. %error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.039826</td>\n",
       "      <td>0.039729</td>\n",
       "      <td>0.040213</td>\n",
       "      <td>0.973236</td>\n",
       "      <td>-0.243309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318847</td>\n",
       "      <td>0.318798</td>\n",
       "      <td>0.324370</td>\n",
       "      <td>1.732260</td>\n",
       "      <td>-0.015195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.350581</td>\n",
       "      <td>0.350533</td>\n",
       "      <td>0.358527</td>\n",
       "      <td>2.266446</td>\n",
       "      <td>-0.013820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.176308</td>\n",
       "      <td>0.176357</td>\n",
       "      <td>0.167393</td>\n",
       "      <td>-5.056334</td>\n",
       "      <td>0.027480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.114438</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.109496</td>\n",
       "      <td>-4.318374</td>\n",
       "      <td>0.127011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       全部数据      分层抽样      随机抽样  随机. %error  分层. %error\n",
       "1  0.039826  0.039729  0.040213    0.973236   -0.243309\n",
       "2  0.318847  0.318798  0.324370    1.732260   -0.015195\n",
       "3  0.350581  0.350533  0.358527    2.266446   -0.013820\n",
       "4  0.176308  0.176357  0.167393   -5.056334    0.027480\n",
       "5  0.114438  0.114583  0.109496   -4.318374    0.127011"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_props"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  测试集处理完成，用分层抽样的训练集数据进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x228a2259348>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y = 'latitude', alpha=0.1) \n",
    "# 将alpha 设置为 0.1 ， 可以看出高密度数据点的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x228a23bd888>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter', x='longitude', y = 'latitude', alpha=0.4,\n",
    "            s=housing['population']/200, label='population',figsize = (10,7),\n",
    "            c= 'median_house_value', cmap = plt.get_cmap('jet'),colorbar=True,\n",
    "            sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "california_img = mpimg.imread('./california.png')\n",
    "\n",
    "housing.plot(kind='scatter', x='longitude', y = 'latitude', alpha=0.4,\n",
    "            s=housing['population']/200, label='population',figsize = (10,7),\n",
    "            c= 'median_house_value', cmap = plt.get_cmap('jet'),colorbar=False,\n",
    "            sharex=False)\n",
    "\n",
    "plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05],\n",
    "          alpha = 0.5, cmap = plt.get_cmap('jet') )\n",
    "plt.ylabel('Latitude',fontsize = 14)\n",
    "plt.xlabel('Longitude',fontsize = 14)\n",
    "\n",
    "price = housing['median_house_value']\n",
    "tick_values = np.linspace(price.min(), price.max(),15)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticklabels([\"$%dk\" % (round(v/1000)) for v in tick_values],fontsize = 14)\n",
    "cbar.set_label('Median House Value', fontsize=16)\n",
    "plt.legend(fontsize=16)\n",
    "# save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 房价与靠海距离，人口密度相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 寻找特征相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.924664</td>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.044568</td>\n",
       "      <td>0.069608</td>\n",
       "      <td>0.099773</td>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.045967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.924664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.144160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.108197</td>\n",
       "      <td>0.011173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.105623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.044568</td>\n",
       "      <td>-0.036100</td>\n",
       "      <td>-0.361262</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>0.134153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.069608</td>\n",
       "      <td>-0.066983</td>\n",
       "      <td>-0.320451</td>\n",
       "      <td>0.930380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.099773</td>\n",
       "      <td>-0.108785</td>\n",
       "      <td>-0.296244</td>\n",
       "      <td>0.857126</td>\n",
       "      <td>0.877747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>-0.024650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.055310</td>\n",
       "      <td>-0.071035</td>\n",
       "      <td>-0.302916</td>\n",
       "      <td>0.918484</td>\n",
       "      <td>0.979728</td>\n",
       "      <td>0.907222</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>0.065843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>-0.015176</td>\n",
       "      <td>-0.079809</td>\n",
       "      <td>-0.119034</td>\n",
       "      <td>0.198050</td>\n",
       "      <td>-0.007723</td>\n",
       "      <td>0.004834</td>\n",
       "      <td>0.013033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>-0.144160</td>\n",
       "      <td>0.105623</td>\n",
       "      <td>0.134153</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>-0.024650</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.688075</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000 -0.924664           -0.108197     0.044568   \n",
       "latitude            -0.924664  1.000000            0.011173    -0.036100   \n",
       "housing_median_age  -0.108197  0.011173            1.000000    -0.361262   \n",
       "total_rooms          0.044568 -0.036100           -0.361262     1.000000   \n",
       "total_bedrooms       0.069608 -0.066983           -0.320451     0.930380   \n",
       "population           0.099773 -0.108785           -0.296244     0.857126   \n",
       "households           0.055310 -0.071035           -0.302916     0.918484   \n",
       "median_income       -0.015176 -0.079809           -0.119034     0.198050   \n",
       "median_house_value  -0.045967 -0.144160            0.105623     0.134153   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                 0.069608    0.099773    0.055310      -0.015176   \n",
       "latitude                 -0.066983   -0.108785   -0.071035      -0.079809   \n",
       "housing_median_age       -0.320451   -0.296244   -0.302916      -0.119034   \n",
       "total_rooms               0.930380    0.857126    0.918484       0.198050   \n",
       "total_bedrooms            1.000000    0.877747    0.979728      -0.007723   \n",
       "population                0.877747    1.000000    0.907222       0.004834   \n",
       "households                0.979728    0.907222    1.000000       0.013033   \n",
       "median_income            -0.007723    0.004834    0.013033       1.000000   \n",
       "median_house_value        0.049686   -0.024650    0.065843       0.688075   \n",
       "\n",
       "                    median_house_value  \n",
       "longitude                    -0.045967  \n",
       "latitude                     -0.144160  \n",
       "housing_median_age            0.105623  \n",
       "total_rooms                   0.134153  \n",
       "total_bedrooms                0.049686  \n",
       "population                   -0.024650  \n",
       "households                    0.065843  \n",
       "median_income                 0.688075  \n",
       "median_house_value            1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr() 计算出每对特征之间的相关系数， 称为皮尔逊相关系数\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000228A2363908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A2283848>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A2328FC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A22F59C8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000228A506E748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A21FF888>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A26CB908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A2716A08>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000228A271E608>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A24627C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A1FB9D48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A215AE08>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000228A2114F48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A20CE088>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A1FF6188>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000228A20352C8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用padans scatter_matrix 函数，可以绘制特征之间的相关性\n",
    "from pandas.plotting import scatter_matrix\n",
    "attributes = ['median_house_value', 'median_income','total_rooms', 'housing_median_age']\n",
    "scatter_matrix(housing[attributes],figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 16, 0, 550000]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter', x='median_income', y = 'median_house_value', alpha = 0.1)\n",
    "plt.axis([0, 16, 0, 550000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     50万美元是一条清晰的线，被设置上限了， 45w，35w，28w，也有虚线， 可能是异常值，我可以在后期把数据删除掉\n",
    "     \n",
    "     1.在机器学习前，识别异常数据，需提前清理掉\n",
    "     2.某些属性“重尾”分布，对其进行转换，计算其对数\n",
    "     3.给机器学习算法输入数据前，尝试各种属性的组合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 尝试不同特征组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value          1.000000\n",
       "median_income               0.688075\n",
       "romms_per_household         0.151948\n",
       "total_rooms                 0.134153\n",
       "housing_median_age          0.105623\n",
       "households                  0.065843\n",
       "total_bedrooms              0.049686\n",
       "population_per_household   -0.023737\n",
       "population                 -0.024650\n",
       "longitude                  -0.045967\n",
       "latitude                   -0.144160\n",
       "bedrooms_per_room          -0.255880\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['romms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_household'] = housing['population']/housing['households']\n",
    "\n",
    "# 关联矩阵\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 机器学习算法数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13643</th>\n",
       "      <td>-117.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>48.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>2.5673</td>\n",
       "      <td>68200.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11573</th>\n",
       "      <td>-118.00</td>\n",
       "      <td>33.77</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>3.4327</td>\n",
       "      <td>192800.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20322</th>\n",
       "      <td>-119.14</td>\n",
       "      <td>34.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.5714</td>\n",
       "      <td>500001.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16484</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>38.25</td>\n",
       "      <td>13.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.6528</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12136</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>244400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6388</th>\n",
       "      <td>-118.03</td>\n",
       "      <td>34.16</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>7.1615</td>\n",
       "      <td>484700.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11197</th>\n",
       "      <td>-117.93</td>\n",
       "      <td>33.84</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>1374.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>3.4750</td>\n",
       "      <td>282500.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20206</th>\n",
       "      <td>-119.22</td>\n",
       "      <td>34.27</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1937.0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>5.0679</td>\n",
       "      <td>234300.0</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>-121.92</td>\n",
       "      <td>37.96</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5332.0</td>\n",
       "      <td>884.0</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>839.0</td>\n",
       "      <td>5.2798</td>\n",
       "      <td>237400.0</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10829</th>\n",
       "      <td>-117.94</td>\n",
       "      <td>33.67</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2552.0</td>\n",
       "      <td>314.0</td>\n",
       "      <td>925.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>8.1839</td>\n",
       "      <td>367000.0</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "13643    -117.33     34.06                48.0        732.0           149.0   \n",
       "11573    -118.00     33.77                24.0       1324.0           267.0   \n",
       "20322    -119.14     34.23                 8.0        243.0            75.0   \n",
       "16484    -121.06     38.25                13.0        651.0           102.0   \n",
       "12136    -117.13     33.89                 4.0       1611.0           239.0   \n",
       "6388     -118.03     34.16                36.0       1401.0           218.0   \n",
       "11197    -117.93     33.84                26.0       2811.0           612.0   \n",
       "20206    -119.22     34.27                30.0       1937.0           295.0   \n",
       "1657     -121.92     37.96                14.0       5332.0           884.0   \n",
       "10829    -117.94     33.67                26.0       2552.0           314.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "13643       486.0       139.0         2.5673             68200.0   \n",
       "11573       687.0       264.0         3.4327            192800.0   \n",
       "20322       102.0        80.0         2.5714            500001.0   \n",
       "16484       301.0       104.0         3.6528            200000.0   \n",
       "12136       275.0        84.0         3.5781            244400.0   \n",
       "6388        667.0       225.0         7.1615            484700.0   \n",
       "11197      1374.0       566.0         3.4750            282500.0   \n",
       "20206       695.0       313.0         5.0679            234300.0   \n",
       "1657       2093.0       839.0         5.2798            237400.0   \n",
       "10829       925.0       323.0         8.1839            367000.0   \n",
       "\n",
       "      ocean_proximity income_cat  \n",
       "13643          INLAND          2  \n",
       "11573       <1H OCEAN          3  \n",
       "20322      NEAR OCEAN          2  \n",
       "16484          INLAND          3  \n",
       "12136          INLAND          3  \n",
       "6388           INLAND          5  \n",
       "11197       <1H OCEAN          3  \n",
       "20206      NEAR OCEAN          4  \n",
       "1657           INLAND          4  \n",
       "10829       <1H OCEAN          5  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 得到分层抽样代表全局数据集的 训练集和测试集\n",
    "strat_train_set.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_labels = strat_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop('median_house_value', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13643</th>\n",
       "      <td>-117.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>48.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>2.5673</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11573</th>\n",
       "      <td>-118.00</td>\n",
       "      <td>33.77</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>3.4327</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20322</th>\n",
       "      <td>-119.14</td>\n",
       "      <td>34.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.5714</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16484</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>38.25</td>\n",
       "      <td>13.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.6528</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12136</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "13643    -117.33     34.06                48.0        732.0           149.0   \n",
       "11573    -118.00     33.77                24.0       1324.0           267.0   \n",
       "20322    -119.14     34.23                 8.0        243.0            75.0   \n",
       "16484    -121.06     38.25                13.0        651.0           102.0   \n",
       "12136    -117.13     33.89                 4.0       1611.0           239.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "13643       486.0       139.0         2.5673          INLAND          2  \n",
       "11573       687.0       264.0         3.4327       <1H OCEAN          3  \n",
       "20322       102.0        80.0         2.5714      NEAR OCEAN          2  \n",
       "16484       301.0       104.0         3.6528          INLAND          3  \n",
       "12136       275.0        84.0         3.5781          INLAND          3  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 13643 to 10725\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16345 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   ocean_proximity     16512 non-null  object  \n",
      " 9   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗\n",
    "   * 大多数机器学算法无法在缺失的特征上工作，创建一些函数辅助，total_bedrooms有缺失，\n",
    "   1. 放弃这些区域\n",
    "   2. 放弃这个属性\n",
    "   3. 将缺失值 设置为 某个值，（0， 平均数或者中位数都可以）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18346</th>\n",
       "      <td>-122.15</td>\n",
       "      <td>37.43</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11709.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7604.0</td>\n",
       "      <td>3589.0</td>\n",
       "      <td>1.9045</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13332</th>\n",
       "      <td>-117.65</td>\n",
       "      <td>34.04</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3393.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>611.0</td>\n",
       "      <td>3.9336</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7316</th>\n",
       "      <td>-118.19</td>\n",
       "      <td>33.98</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4179.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4582.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>2.0087</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2301</th>\n",
       "      <td>-119.78</td>\n",
       "      <td>36.82</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2133.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>3.6250</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11741</th>\n",
       "      <td>-121.13</td>\n",
       "      <td>38.87</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1127.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>530.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3.0917</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "18346    -122.15     37.43                20.0      11709.0             NaN   \n",
       "13332    -117.65     34.04                15.0       3393.0             NaN   \n",
       "7316     -118.19     33.98                36.0       4179.0             NaN   \n",
       "2301     -119.78     36.82                25.0       5016.0             NaN   \n",
       "11741    -121.13     38.87                48.0       1127.0             NaN   \n",
       "\n",
       "       population  households  median_income ocean_proximity income_cat  \n",
       "18346      7604.0      3589.0         1.9045        NEAR BAY          2  \n",
       "13332      2039.0       611.0         3.9336          INLAND          3  \n",
       "7316       4582.0      1196.0         2.0087       <1H OCEAN          2  \n",
       "2301       2133.0       928.0         3.6250          INLAND          3  \n",
       "11741       530.0       186.0         3.0917          INLAND          3  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis=1)]\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(add_indicator=False, copy=True, fill_value=None,\n",
       "              missing_values=nan, strategy='median', verbose=0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 放弃数据\n",
    "# rows.dropna(subset = ['total_bedrooms'])\n",
    "# rows.head()\n",
    "# # 放弃该属性\n",
    "# rows.drop('total_bedrooms', axis=1)\n",
    "\n",
    "# 将缺失值设置为某个值，（0， 平均树或者中位数）\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "\n",
    "# 使用fit() 方法将 imputer实例适配到训练集\n",
    "\n",
    "housing_num = housing.drop('ocean_proximity', axis=1)\n",
    "\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.5   ,   34.26  ,   29.    , 2122.    ,  433.    , 1164.    ,\n",
       "        407.    ,    3.5357,    3.    ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-118.5   ,   34.26  ,   29.    , 2122.    ,  433.    , 1164.    ,\n",
       "        407.    ,    3.5357,    3.    ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-117.33  ,   34.06  ,   48.    , ...,  139.    ,    2.5673,\n",
       "           2.    ],\n",
       "       [-118.    ,   33.77  ,   24.    , ...,  264.    ,    3.4327,\n",
       "           3.    ],\n",
       "       [-119.14  ,   34.23  ,    8.    , ...,   80.    ,    2.5714,\n",
       "           2.    ],\n",
       "       ...,\n",
       "       [-118.03  ,   34.06  ,   24.    , ...,  712.    ,    2.5445,\n",
       "           2.    ],\n",
       "       [-119.77  ,   36.86  ,    7.    , ...,  562.    ,    8.2737,\n",
       "           5.    ],\n",
       "       [-117.82  ,   33.64  ,   18.    , ...,  278.    ,    9.8589,\n",
       "           5.    ]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
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       "      <th>13643</th>\n",
       "      <td>-117.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>48.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>2.5673</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>11573</th>\n",
       "      <td>-118.00</td>\n",
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       "      <td>1324.0</td>\n",
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       "      <td>687.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>3.4327</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20322</th>\n",
       "      <td>-119.14</td>\n",
       "      <td>34.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.5714</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>16484</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>38.25</td>\n",
       "      <td>13.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.6528</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12136</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "13643    -117.33     34.06                48.0        732.0           149.0   \n",
       "11573    -118.00     33.77                24.0       1324.0           267.0   \n",
       "20322    -119.14     34.23                 8.0        243.0            75.0   \n",
       "16484    -121.06     38.25                13.0        651.0           102.0   \n",
       "12136    -117.13     33.89                 4.0       1611.0           239.0   \n",
       "\n",
       "       population  households  median_income  income_cat  \n",
       "13643       486.0       139.0         2.5673         2.0  \n",
       "11573       687.0       264.0         3.4327         3.0  \n",
       "20322       102.0        80.0         2.5714         2.0  \n",
       "16484       301.0       104.0         3.6528         3.0  \n",
       "12136       275.0        84.0         3.5781         3.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X, columns=housing_num.columns, index= housing_num.index)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 13643 to 10725\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           16512 non-null  float64\n",
      " 1   latitude            16512 non-null  float64\n",
      " 2   housing_median_age  16512 non-null  float64\n",
      " 3   total_rooms         16512 non-null  float64\n",
      " 4   total_bedrooms      16512 non-null  float64\n",
      " 5   population          16512 non-null  float64\n",
      " 6   households          16512 non-null  float64\n",
      " 7   median_income       16512 non-null  float64\n",
      " 8   income_cat          16512 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "housing_tr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-Learn\n",
    "* 一致性\n",
    "   1. 估算器\n",
    "       比如：各种机器学习算法\n",
    "       fit()执行估算器\n",
    "\n",
    "   2. 转换器  \n",
    "       比如：LabelBinarizer\n",
    "       transform()  执行转换数据集\n",
    "       fit_transform()  先估算，再转换\n",
    "   3. 预测器\n",
    "       predict()  对给定的新数据集进行预测\n",
    "       score()    评估测试集的预测质量\n",
    "* 检查\n",
    "    1. imputer.strategy_学习参数通过公共实例变量访问"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13643        INLAND\n",
       "11573     <1H OCEAN\n",
       "20322    NEAR OCEAN\n",
       "16484        INLAND\n",
       "12136        INLAND\n",
       "Name: ocean_proximity, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     7307\n",
       "INLAND        5225\n",
       "NEAR OCEAN    2123\n",
       "NEAR BAY      1852\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 4, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 文本转换为对应数字分类，使用转换器\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoder = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1.机器学习算法会以为两个相近的数字比远的数字更类似；\n",
    "    2.独热编码；\n",
    "    3.oneHotEnconder编码器，fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 4, ..., 0, 1, 0])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [0],\n",
       "       [4],\n",
       "       ...,\n",
       "       [0],\n",
       "       [1],\n",
       "       [0]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_hot = encoder.fit_transform(housing_cat_encoder.reshape(-1,1))\n",
    "housing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1.],\n",
       "       ...,\n",
       "       [1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#稀疏矩阵转成numpy\n",
    "housing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一次完成两次转换 文本 to 整数类型 to 独热类型\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int32'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输出稀疏矩阵\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output = True)\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 自定义转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = [list(housing.columns).index(col) for col in (\"total_rooms\", \"total_bedrooms\",\"population\",\"households\")]\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y =None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household =X[:,rooms_ix] / X[:,household_ix]\n",
    "        population_per_household = X[:,population_ix] / X[:,household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:,bedrooms_ix] / X[:,rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "        \n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(housing.columns).index(\"total_rooms\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-117.33, 34.06, 48.0, ..., 2, 5.266187050359712,\n",
       "        3.4964028776978417],\n",
       "       [-118.0, 33.77, 24.0, ..., 3, 5.015151515151516,\n",
       "        2.602272727272727],\n",
       "       [-119.14, 34.23, 8.0, ..., 2, 3.0375, 1.275],\n",
       "       ...,\n",
       "       [-118.03, 34.06, 24.0, ..., 2, 3.467696629213483,\n",
       "        5.362359550561798],\n",
       "       [-119.77, 36.86, 7.0, ..., 5, 7.364768683274021,\n",
       "        3.2793594306049823],\n",
       "       [-117.82, 33.64, 18.0, ..., 5, 7.100719424460432,\n",
       "        2.906474820143885]], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-117.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>48</td>\n",
       "      <td>732</td>\n",
       "      <td>149</td>\n",
       "      <td>486</td>\n",
       "      <td>139</td>\n",
       "      <td>2.5673</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.26619</td>\n",
       "      <td>3.4964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-118</td>\n",
       "      <td>33.77</td>\n",
       "      <td>24</td>\n",
       "      <td>1324</td>\n",
       "      <td>267</td>\n",
       "      <td>687</td>\n",
       "      <td>264</td>\n",
       "      <td>3.4327</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.01515</td>\n",
       "      <td>2.60227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-119.14</td>\n",
       "      <td>34.23</td>\n",
       "      <td>8</td>\n",
       "      <td>243</td>\n",
       "      <td>75</td>\n",
       "      <td>102</td>\n",
       "      <td>80</td>\n",
       "      <td>2.5714</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0375</td>\n",
       "      <td>1.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>38.25</td>\n",
       "      <td>13</td>\n",
       "      <td>651</td>\n",
       "      <td>102</td>\n",
       "      <td>301</td>\n",
       "      <td>104</td>\n",
       "      <td>3.6528</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>6.25962</td>\n",
       "      <td>2.89423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4</td>\n",
       "      <td>1611</td>\n",
       "      <td>239</td>\n",
       "      <td>275</td>\n",
       "      <td>84</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>19.1786</td>\n",
       "      <td>3.27381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0      1   2     3    4    5    6       7           8  9       10  \\\n",
       "0 -117.33  34.06  48   732  149  486  139  2.5673      INLAND  2  5.26619   \n",
       "1    -118  33.77  24  1324  267  687  264  3.4327   <1H OCEAN  3  5.01515   \n",
       "2 -119.14  34.23   8   243   75  102   80  2.5714  NEAR OCEAN  2   3.0375   \n",
       "3 -121.06  38.25  13   651  102  301  104  3.6528      INLAND  3  6.25962   \n",
       "4 -117.13  33.89   4  1611  239  275   84  3.5781      INLAND  3  19.1786   \n",
       "\n",
       "        11  \n",
       "0   3.4964  \n",
       "1  2.60227  \n",
       "2    1.275  \n",
       "3  2.89423  \n",
       "4  3.27381  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13643</th>\n",
       "      <td>-117.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>48</td>\n",
       "      <td>732</td>\n",
       "      <td>149</td>\n",
       "      <td>486</td>\n",
       "      <td>139</td>\n",
       "      <td>2.5673</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.26619</td>\n",
       "      <td>3.4964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11573</th>\n",
       "      <td>-118</td>\n",
       "      <td>33.77</td>\n",
       "      <td>24</td>\n",
       "      <td>1324</td>\n",
       "      <td>267</td>\n",
       "      <td>687</td>\n",
       "      <td>264</td>\n",
       "      <td>3.4327</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.01515</td>\n",
       "      <td>2.60227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20322</th>\n",
       "      <td>-119.14</td>\n",
       "      <td>34.23</td>\n",
       "      <td>8</td>\n",
       "      <td>243</td>\n",
       "      <td>75</td>\n",
       "      <td>102</td>\n",
       "      <td>80</td>\n",
       "      <td>2.5714</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0375</td>\n",
       "      <td>1.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16484</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>38.25</td>\n",
       "      <td>13</td>\n",
       "      <td>651</td>\n",
       "      <td>102</td>\n",
       "      <td>301</td>\n",
       "      <td>104</td>\n",
       "      <td>3.6528</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>6.25962</td>\n",
       "      <td>2.89423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12136</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4</td>\n",
       "      <td>1611</td>\n",
       "      <td>239</td>\n",
       "      <td>275</td>\n",
       "      <td>84</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>19.1786</td>\n",
       "      <td>3.27381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "13643   -117.33    34.06                 48         732            149   \n",
       "11573      -118    33.77                 24        1324            267   \n",
       "20322   -119.14    34.23                  8         243             75   \n",
       "16484   -121.06    38.25                 13         651            102   \n",
       "12136   -117.13    33.89                  4        1611            239   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "13643        486        139        2.5673          INLAND          2   \n",
       "11573        687        264        3.4327       <1H OCEAN          3   \n",
       "20322        102         80        2.5714      NEAR OCEAN          2   \n",
       "16484        301        104        3.6528          INLAND          3   \n",
       "12136        275         84        3.5781          INLAND          3   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "13643             5.26619                   3.4964  \n",
       "11573             5.01515                  2.60227  \n",
       "20322              3.0375                    1.275  \n",
       "16484             6.25962                  2.89423  \n",
       "12136             19.1786                  3.27381  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns=list(housing.columns)+[\"rooms_per_household\",\"population_per_household\"],\n",
    "    index=housing.index)\n",
    "\n",
    "housing_extra_attribs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14168</th>\n",
       "      <td>-117.06</td>\n",
       "      <td>32.76</td>\n",
       "      <td>36</td>\n",
       "      <td>2785</td>\n",
       "      <td>577</td>\n",
       "      <td>1275</td>\n",
       "      <td>527</td>\n",
       "      <td>2.3015</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>5.28463</td>\n",
       "      <td>2.41935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14412</th>\n",
       "      <td>-117.23</td>\n",
       "      <td>32.78</td>\n",
       "      <td>35</td>\n",
       "      <td>1649</td>\n",
       "      <td>355</td>\n",
       "      <td>746</td>\n",
       "      <td>360</td>\n",
       "      <td>4.6293</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>4.58056</td>\n",
       "      <td>2.07222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3780</th>\n",
       "      <td>-118.39</td>\n",
       "      <td>34.17</td>\n",
       "      <td>26</td>\n",
       "      <td>6429</td>\n",
       "      <td>1611</td>\n",
       "      <td>2806</td>\n",
       "      <td>1491</td>\n",
       "      <td>3.1929</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>4.31187</td>\n",
       "      <td>1.88196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13540</th>\n",
       "      <td>-117.3</td>\n",
       "      <td>34.14</td>\n",
       "      <td>39</td>\n",
       "      <td>1781</td>\n",
       "      <td>335</td>\n",
       "      <td>841</td>\n",
       "      <td>320</td>\n",
       "      <td>1.9432</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.56562</td>\n",
       "      <td>2.62812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6101</th>\n",
       "      <td>-117.88</td>\n",
       "      <td>34.12</td>\n",
       "      <td>35</td>\n",
       "      <td>1574</td>\n",
       "      <td>276</td>\n",
       "      <td>1088</td>\n",
       "      <td>289</td>\n",
       "      <td>4.0938</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>5.44637</td>\n",
       "      <td>3.76471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5080</th>\n",
       "      <td>-118.3</td>\n",
       "      <td>33.98</td>\n",
       "      <td>48</td>\n",
       "      <td>2010</td>\n",
       "      <td>445</td>\n",
       "      <td>1208</td>\n",
       "      <td>404</td>\n",
       "      <td>1.6611</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.97525</td>\n",
       "      <td>2.9901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10058</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>39.29</td>\n",
       "      <td>14</td>\n",
       "      <td>1864</td>\n",
       "      <td>331</td>\n",
       "      <td>894</td>\n",
       "      <td>332</td>\n",
       "      <td>3.4028</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>3</td>\n",
       "      <td>5.61446</td>\n",
       "      <td>2.69277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4993</th>\n",
       "      <td>-118.31</td>\n",
       "      <td>33.99</td>\n",
       "      <td>44</td>\n",
       "      <td>1703</td>\n",
       "      <td>358</td>\n",
       "      <td>789</td>\n",
       "      <td>249</td>\n",
       "      <td>1.7083</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>6.83936</td>\n",
       "      <td>3.16867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12623</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>38.48</td>\n",
       "      <td>5</td>\n",
       "      <td>27870</td>\n",
       "      <td>5027</td>\n",
       "      <td>11935</td>\n",
       "      <td>4855</td>\n",
       "      <td>4.8811</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>5.74047</td>\n",
       "      <td>2.45829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3231</th>\n",
       "      <td>-119.57</td>\n",
       "      <td>36.27</td>\n",
       "      <td>20</td>\n",
       "      <td>2673</td>\n",
       "      <td>452</td>\n",
       "      <td>1394</td>\n",
       "      <td>449</td>\n",
       "      <td>2.625</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>2</td>\n",
       "      <td>5.95323</td>\n",
       "      <td>3.10468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>-118.46</td>\n",
       "      <td>34.22</td>\n",
       "      <td>35</td>\n",
       "      <td>2288</td>\n",
       "      <td>617</td>\n",
       "      <td>2222</td>\n",
       "      <td>566</td>\n",
       "      <td>2.6299</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0424</td>\n",
       "      <td>3.9258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13167</th>\n",
       "      <td>-121.37</td>\n",
       "      <td>36.83</td>\n",
       "      <td>14</td>\n",
       "      <td>3658</td>\n",
       "      <td>612</td>\n",
       "      <td>1951</td>\n",
       "      <td>600</td>\n",
       "      <td>4.76</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>6.09667</td>\n",
       "      <td>3.25167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8237</th>\n",
       "      <td>-118.19</td>\n",
       "      <td>33.77</td>\n",
       "      <td>52</td>\n",
       "      <td>1562</td>\n",
       "      <td>616</td>\n",
       "      <td>692</td>\n",
       "      <td>512</td>\n",
       "      <td>1.4048</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.05078</td>\n",
       "      <td>1.35156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17578</th>\n",
       "      <td>-121.94</td>\n",
       "      <td>37.3</td>\n",
       "      <td>26</td>\n",
       "      <td>4348</td>\n",
       "      <td>814</td>\n",
       "      <td>2347</td>\n",
       "      <td>810</td>\n",
       "      <td>4.7275</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>5.3679</td>\n",
       "      <td>2.89753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18014</th>\n",
       "      <td>-121.96</td>\n",
       "      <td>37.27</td>\n",
       "      <td>22</td>\n",
       "      <td>6114</td>\n",
       "      <td>1211</td>\n",
       "      <td>2983</td>\n",
       "      <td>1163</td>\n",
       "      <td>5.2533</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>5.25709</td>\n",
       "      <td>2.56492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "14168   -117.06    32.76                 36        2785            577   \n",
       "14412   -117.23    32.78                 35        1649            355   \n",
       "3780    -118.39    34.17                 26        6429           1611   \n",
       "13540    -117.3    34.14                 39        1781            335   \n",
       "6101    -117.88    34.12                 35        1574            276   \n",
       "5080     -118.3    33.98                 48        2010            445   \n",
       "10058   -121.06    39.29                 14        1864            331   \n",
       "4993    -118.31    33.99                 44        1703            358   \n",
       "12623   -121.53    38.48                  5       27870           5027   \n",
       "3231    -119.57    36.27                 20        2673            452   \n",
       "3642    -118.46    34.22                 35        2288            617   \n",
       "13167   -121.37    36.83                 14        3658            612   \n",
       "8237    -118.19    33.77                 52        1562            616   \n",
       "17578   -121.94     37.3                 26        4348            814   \n",
       "18014   -121.96    37.27                 22        6114           1211   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "14168       1275        527        2.3015      NEAR OCEAN          2   \n",
       "14412        746        360        4.6293      NEAR OCEAN          4   \n",
       "3780        2806       1491        3.1929       <1H OCEAN          3   \n",
       "13540        841        320        1.9432          INLAND          2   \n",
       "6101        1088        289        4.0938       <1H OCEAN          3   \n",
       "5080        1208        404        1.6611       <1H OCEAN          2   \n",
       "10058        894        332        3.4028          INLAND          3   \n",
       "4993         789        249        1.7083       <1H OCEAN          2   \n",
       "12623      11935       4855        4.8811          INLAND          4   \n",
       "3231        1394        449         2.625          INLAND          2   \n",
       "3642        2222        566        2.6299       <1H OCEAN          2   \n",
       "13167       1951        600          4.76          INLAND          4   \n",
       "8237         692        512        1.4048      NEAR OCEAN          1   \n",
       "17578       2347        810        4.7275       <1H OCEAN          4   \n",
       "18014       2983       1163        5.2533       <1H OCEAN          4   \n",
       "\n",
       "      rooms_per_household population_per_household  \n",
       "14168             5.28463                  2.41935  \n",
       "14412             4.58056                  2.07222  \n",
       "3780              4.31187                  1.88196  \n",
       "13540             5.56562                  2.62812  \n",
       "6101              5.44637                  3.76471  \n",
       "5080              4.97525                   2.9901  \n",
       "10058             5.61446                  2.69277  \n",
       "4993              6.83936                  3.16867  \n",
       "12623             5.74047                  2.45829  \n",
       "3231              5.95323                  3.10468  \n",
       "3642               4.0424                   3.9258  \n",
       "13167             6.09667                  3.25167  \n",
       "8237              3.05078                  1.35156  \n",
       "17578              5.3679                  2.89753  \n",
       "18014             5.25709                  2.56492  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs.sample(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征缩放\n",
    "\n",
    "    最重要也是最需要应用在数据上的转换器，就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "    房间总数 范围6到39320，收入中位数范围0到15\n",
    "    目标值通常不需要缩放\n",
    "    同比缩放所有属性，2种方法 最小-最大缩放和标准化\n",
    "    最小-最大缩放，又叫归一化，将值重新缩放到0到1之间，将值减去最小值并除以最大值和最小值差，如果你不希望是0到1，可以调整超参数feature_range\n",
    "    标准化 减去平均值 ，所以标准化均值总是0，然后除以方差，结果的分布具备单位方差，不同于归一化，标准化不将值绑定到特定范围，受异常值影响小\n",
    "    缩放器仅用来拟合训练集，不是完成的数据集\n",
    "    \n",
    "### 转换流水线\n",
    "    许多数据转换的步骤需要以正确的顺序来执行， PipeLine来支持这样的转换\n",
    "    pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转换器，必须有fit_fransform()方法\n",
    "    调用流水芡的fit方法时，会在所有转换器上按照顺序依次调用fit_transform()，将一个调用的输出作为参数传递给下一个调用方法，直到传递到最终\n",
    "    估算器，只会调用fit方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer', SimpleImputer(strategy=\"median\")),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler', StandardScaler())\n",
    "    \n",
    "])\n",
    "\n",
    "housing_num_tr  = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 13643 to 10725\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  float64 \n",
      " 3   total_rooms         16512 non-null  float64 \n",
      " 4   total_bedrooms      16345 non-null  float64 \n",
      " 5   population          16512 non-null  float64 \n",
      " 6   households          16512 non-null  float64 \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   income_cat          16512 non-null  category\n",
      "dtypes: category(1), float64(8)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "housing_num.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataframe to series to ndarray\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attributes_names):\n",
    "        self.attributes_names = attributes_names\n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    def transform(self, X, y =None):\n",
    "        return X[self.attributes_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['longitude',\n",
       " 'latitude',\n",
       " 'housing_median_age',\n",
       " 'total_rooms',\n",
       " 'total_bedrooms',\n",
       " 'population',\n",
       " 'households',\n",
       " 'median_income',\n",
       " 'income_cat']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_attribs = list(housing_num)\n",
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13643</th>\n",
       "      <td>-117.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>48.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>2.5673</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11573</th>\n",
       "      <td>-118.00</td>\n",
       "      <td>33.77</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>3.4327</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20322</th>\n",
       "      <td>-119.14</td>\n",
       "      <td>34.23</td>\n",
       "      <td>8.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.5714</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16484</th>\n",
       "      <td>-121.06</td>\n",
       "      <td>38.25</td>\n",
       "      <td>13.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.6528</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12136</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>33.89</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>3.5781</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10364</th>\n",
       "      <td>-117.68</td>\n",
       "      <td>33.61</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2962.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>6.0689</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18097</th>\n",
       "      <td>-122.05</td>\n",
       "      <td>37.33</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3674.0</td>\n",
       "      <td>824.0</td>\n",
       "      <td>1364.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>6.3131</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6537</th>\n",
       "      <td>-118.03</td>\n",
       "      <td>34.06</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2469.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>3818.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>2.5445</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>-119.77</td>\n",
       "      <td>36.86</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4139.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>1843.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>8.2737</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10725</th>\n",
       "      <td>-117.82</td>\n",
       "      <td>33.64</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>808.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>9.8589</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "13643    -117.33     34.06                48.0        732.0           149.0   \n",
       "11573    -118.00     33.77                24.0       1324.0           267.0   \n",
       "20322    -119.14     34.23                 8.0        243.0            75.0   \n",
       "16484    -121.06     38.25                13.0        651.0           102.0   \n",
       "12136    -117.13     33.89                 4.0       1611.0           239.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "10364    -117.68     33.61                19.0       2962.0           405.0   \n",
       "18097    -122.05     37.33                17.0       3674.0           824.0   \n",
       "6537     -118.03     34.06                24.0       2469.0           731.0   \n",
       "2227     -119.77     36.86                 7.0       4139.0           544.0   \n",
       "10725    -117.82     33.64                18.0       1974.0           260.0   \n",
       "\n",
       "       population  households  median_income income_cat  \n",
       "13643       486.0       139.0         2.5673          2  \n",
       "11573       687.0       264.0         3.4327          3  \n",
       "20322       102.0        80.0         2.5714          2  \n",
       "16484       301.0       104.0         3.6528          3  \n",
       "12136       275.0        84.0         3.5781          3  \n",
       "...           ...         ...            ...        ...  \n",
       "10364      1295.0       440.0         6.0689          5  \n",
       "18097      1364.0       694.0         6.3131          5  \n",
       "6537       3818.0       712.0         2.5445          2  \n",
       "2227       1843.0       562.0         8.2737          5  \n",
       "10725       808.0       278.0         9.8589          5  \n",
       "\n",
       "[16512 rows x 9 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    ('selector',DataFrameSelector(num_attribs)),\n",
    "    ('imputerm',SimpleImputer(strategy=\"median\")),\n",
    "    ('attribs_adder', CombinedAttributesAdder()),\n",
    "    ('std_scaler', StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        self.encoder = LabelBinarizer(*args, **kwargs)\n",
    "    def fit(self, x, y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self, x, y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('selector', DataFrameSelector(cat_attribs)),\n",
    "    ('LabelBinarizerb',MyLabelBinarizer()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline =FeatureUnion(transformer_list=[\n",
    "    ('num_pipeline', num_pipeline),\n",
    "    ('cat_pipeline', cat_pipeline),\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.12238154, -0.73916342,  1.54019195, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.78812581, -0.87470615, -0.3699156 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.21939218, -0.65970734, -1.64332064, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ...,\n",
       "       [ 0.77315913, -0.73916342, -0.3699156 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.09490798,  0.56952498, -1.72290845, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.87792585, -0.93546668, -0.84744249, ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.219195</td>\n",
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       "      <td>-0.903285</td>\n",
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       "      <td>-1.029350</td>\n",
       "      <td>-0.114620</td>\n",
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       "      <td>0.357986</td>\n",
       "      <td>-0.014516</td>\n",
       "      <td>-0.874724</td>\n",
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       "      <td>-0.818620</td>\n",
       "      <td>-1.961672</td>\n",
       "      <td>-0.466164</td>\n",
       "      <td>-0.707202</td>\n",
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       "      <td>-1.081419</td>\n",
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       "    <tr>\n",
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       "      <td>-0.739163</td>\n",
       "      <td>-0.369916</td>\n",
       "      <td>-0.075486</td>\n",
       "      <td>0.462641</td>\n",
       "      <td>2.084515</td>\n",
       "      <td>0.553549</td>\n",
       "      <td>-0.699620</td>\n",
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       "      <td>-0.835595</td>\n",
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       "    <tr>\n",
       "      <th>16510</th>\n",
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       "      <td>-0.538513</td>\n",
       "      <td>-0.576349</td>\n",
       "      <td>3.161177</td>\n",
       "      <td>1.890305</td>\n",
       "      <td>0.717568</td>\n",
       "      <td>-0.013279</td>\n",
       "      <td>-1.256692</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16512 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3         4         5         6  \\\n",
       "0      1.122382 -0.739163  1.540192 -0.866403 -0.921197 -0.819116 -0.938229   \n",
       "1      0.788126 -0.874706 -0.369916 -0.596845 -0.640625 -0.643957 -0.612797   \n",
       "2      0.219392 -0.659707 -1.643321 -1.089062 -1.097149 -1.153748 -1.091833   \n",
       "3     -0.738475  1.219195 -1.245382 -0.903285 -1.032950 -0.980332 -1.029350   \n",
       "4      1.222159 -0.818620 -1.961672 -0.466164 -0.707202 -1.002989 -1.081419   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "16507  0.947770 -0.949488 -0.767855  0.148994 -0.312499 -0.114123 -0.154590   \n",
       "16508 -1.232375  0.789198 -0.927030  0.473192  0.683770 -0.053994  0.506687   \n",
       "16509  0.773159 -0.739163 -0.369916 -0.075486  0.462641  2.084515  0.553549   \n",
       "16510 -0.094908  0.569525 -1.722908  0.684923  0.018006  0.363425  0.163031   \n",
       "16511  0.877926 -0.935467 -0.847442 -0.300877 -0.657269 -0.538513 -0.576349   \n",
       "\n",
       "              7         8         9        10        11   12   13   14   15  \\\n",
       "0     -0.687585 -0.954456 -0.066718  0.046340 -0.157745  0.0  1.0  0.0  0.0   \n",
       "1     -0.230796 -0.006202 -0.174038 -0.044021 -0.186661  1.0  0.0  0.0  0.0   \n",
       "2     -0.685421 -0.954456 -1.019510 -0.178157  1.449843  0.0  0.0  0.0  0.0   \n",
       "3     -0.114620 -0.006202  0.357986 -0.014516 -0.874724  0.0  1.0  0.0  0.0   \n",
       "4     -0.154049 -0.006202  5.881004  0.023845 -1.002104  0.0  1.0  0.0  0.0   \n",
       "...         ...       ...       ...       ...       ...  ...  ...  ...  ...   \n",
       "16507  1.160682  1.890305  0.559858 -0.009569 -1.179905  1.0  0.0  0.0  0.0   \n",
       "16508  1.289580  1.890305 -0.054849 -0.108383  0.159318  1.0  0.0  0.0  0.0   \n",
       "16509 -0.699620 -0.954456 -0.835595  0.234915  1.257546  1.0  0.0  0.0  0.0   \n",
       "16510  2.324453  1.890305  0.830453  0.024405 -1.260969  0.0  1.0  0.0  0.0   \n",
       "16511  3.161177  1.890305  0.717568 -0.013279 -1.256692  1.0  0.0  0.0  0.0   \n",
       "\n",
       "        16  \n",
       "0      0.0  \n",
       "1      0.0  \n",
       "2      1.0  \n",
       "3      0.0  \n",
       "4      0.0  \n",
       "...    ...  \n",
       "16507  0.0  \n",
       "16508  0.0  \n",
       "16509  0.0  \n",
       "16510  0.0  \n",
       "16511  0.0  \n",
       "\n",
       "[16512 rows x 17 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_prepared)\n",
    "housing_prepared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择和训练模型\n",
    "* 获得了数据\n",
    "* 数据探索\n",
    "* 对训练集和测试集进行拆分\n",
    "* 编写了转换数据流水线\n",
    "* 自动清理和准备机器学习算法的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13643     68200.0\n",
       "11573    192800.0\n",
       "20322    500001.0\n",
       "16484    200000.0\n",
       "12136    244400.0\n",
       "           ...   \n",
       "10364    248000.0\n",
       "18097    436400.0\n",
       "6537     151400.0\n",
       "2227     193500.0\n",
       "10725    500001.0\n",
       "Name: median_house_value, Length: 16512, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(housing_prepared)\n",
    "housing_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型和评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions [114115.23215793 198619.65967604 173541.63884212 116297.11394676\n",
      " 136234.15554167]\n"
     ]
    }
   ],
   "source": [
    "# 预测数据\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions\", lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67805.13693174836"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_mse\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse\n",
    "\n",
    "# 大多数地区的房屋中位数 在120000到265000美元之间，预测误差高达 68628,这是一个典型的模型对训练数据拟合不足的案例，\n",
    "# 原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大，\n",
    "# 1. 选择强大的模型，2 为算法提供更好的特征，3.减少对模型的限制等方法，\n",
    "\n",
    "# 决策树可以找到复杂的非线性关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=42, splitter='best')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(housing_prepared,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     完美，也可能是这个模型对数据严重过度拟合了，如何确认？轻易不要启动测试集，拿训练集中的一部分用于训练，另一部分用于模型的验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用交叉验证来更好的进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([73789.22137554, 71144.68442518, 68167.86965528, 73294.14077373,\n",
       "       69350.36648229, 69643.66453493, 70914.19606507, 68986.28723658,\n",
       "       70199.70667233, 66889.54827537])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用train_test_split函数将训练集分为较小的训练集和验证集，然后根据这些较小的训练集来训练模型，并对其进行评估\n",
    "# sklearn的交叉验证，将训练集随机分割成10个不同的子集，每个子集称为一个折叠，对模型进行10次训练和评估，每次挑选1个折叠进行评估，另外9个进行训练\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# neg_mean_squared_error‘ 也就是 均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "\n",
    "# 决策树交叉验证\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                         scoring=\"neg_mean_squared_error\", cv=10)  # tree_reg 是数模型\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [73789.22137554 71144.68442518 68167.86965528 73294.14077373\n",
      " 69350.36648229 69643.66453493 70914.19606507 68986.28723658\n",
      " 70199.70667233 66889.54827537]\n",
      "Mean: 70237.96854962983\n",
      "Standard deviation: 2035.618706107785\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\",scores)\n",
    "    print(\"Mean:\",scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "    \n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [71448.11502404 70456.0685261  64753.61772721 67936.01603807\n",
      " 66768.80840274 68449.25553265 68739.85675129 67055.81384301\n",
      " 72309.36379514 62974.17509453]\n",
      "Mean: 68089.10907347834\n",
      "Standard deviation: 2745.387558363243\n"
     ]
    }
   ],
   "source": [
    "# 线性 交叉验证\n",
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=10, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [54808.63210918 54108.78780656 52346.85267551 53925.64333852\n",
      " 51810.62613611 55442.43192922 51517.03447325 51566.41348883\n",
      " 53975.08015913 45781.76448157]\n",
      "Mean: 52528.32665978659\n",
      "Standard deviation: 2612.419690575175\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared,housing_labels,\n",
    "                               scoring = \"neg_mean_squared_error\", cv=10)\n",
    "\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型调参和网格搜索\n",
    "    1.手动调参难\n",
    "    2.用GridSearchCV搜索参数告诉它，进行试验的超参数是什么，和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = [\n",
    "    {'n_estimators':[3,10,30], 'max_features':[2,4,6,8]},\n",
    "    {'bootstrap':[False], 'n_estimators':[3,10], 'max_features':[2,3,4]},\n",
    "]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                          scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 8, 'n_estimators': 30}"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features=8, max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=30, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63683.075008789245 {'max_features': 2, 'n_estimators': 3}\n",
      "55681.69422455634 {'max_features': 2, 'n_estimators': 10}\n",
      "53046.893656305365 {'max_features': 2, 'n_estimators': 30}\n",
      "61051.54922400591 {'max_features': 4, 'n_estimators': 3}\n",
      "53724.203353878744 {'max_features': 4, 'n_estimators': 10}\n",
      "51225.428987760584 {'max_features': 4, 'n_estimators': 30}\n",
      "59449.900285950425 {'max_features': 6, 'n_estimators': 3}\n",
      "53105.762865923905 {'max_features': 6, 'n_estimators': 10}\n",
      "50747.57148351755 {'max_features': 6, 'n_estimators': 30}\n",
      "59373.834261802505 {'max_features': 8, 'n_estimators': 3}\n",
      "52888.46212002997 {'max_features': 8, 'n_estimators': 10}\n",
      "50679.923485583364 {'max_features': 8, 'n_estimators': 30}\n",
      "61726.40259451409 {'bootstrap': False, 'max_features': 2, 'n_estimators': 3}\n",
      "54118.807699547884 {'bootstrap': False, 'max_features': 2, 'n_estimators': 10}\n",
      "59934.87332924858 {'bootstrap': False, 'max_features': 3, 'n_estimators': 3}\n",
      "52579.66570602339 {'bootstrap': False, 'max_features': 3, 'n_estimators': 10}\n",
      "58852.78774142635 {'bootstrap': False, 'max_features': 4, 'n_estimators': 3}\n",
      "52377.21384582086 {'bootstrap': False, 'max_features': 4, 'n_estimators': 10}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_rearch.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score),params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据准备步骤也可以当作超参数来处理，网格搜索会自动查找是否添加你不确定的特征，比如是否使用转换器 combinedAttre的超参数add_bedrooms_per_rom\n",
    "# 也可是使用它自动寻找处理问题的最佳方法，比如异常值，缺失特征，特征选择等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   ccp_alpha=0.0,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   max_samples=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=100,\n",
       "                                                   n_jobs=None, oob_score=Fals...\n",
       "                   iid='deprecated', n_iter=10, n_jobs=None,\n",
       "                   param_distributions={'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000228B86D7A48>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000228B86D7A08>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "from scipy.stats import randint \n",
    "\n",
    "param_distribs = {\n",
    "    'n_estimators':randint(low=1,high=50),\n",
    "    'max_features':randint(low=1,high=8),\n",
    "}\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                               n_iter=10,cv=5,scoring='neg_mean_squared_error', random_state=42)\n",
    "\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50759.133663431596 {'max_features': 7, 'n_estimators': 29}\n",
      "50195.86735769813 {'max_features': 7, 'n_estimators': 43}\n",
      "51173.19191966629 {'max_features': 5, 'n_estimators': 21}\n",
      "51237.45031741004 {'max_features': 7, 'n_estimators': 19}\n",
      "52624.657318821206 {'max_features': 7, 'n_estimators': 11}\n",
      "51771.31117205325 {'max_features': 3, 'n_estimators': 24}\n",
      "50677.45097595772 {'max_features': 5, 'n_estimators': 36}\n",
      "51899.151737959444 {'max_features': 3, 'n_estimators': 22}\n",
      "65169.58101138231 {'max_features': 5, 'n_estimators': 2}\n",
      "51225.428987760584 {'max_features': 4, 'n_estimators': 30}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.77213304e-02, 5.76663215e-02, 4.42669199e-02, 1.71726774e-02,\n",
       "       1.67899604e-02, 1.68960394e-02, 1.53692140e-02, 3.16570866e-01,\n",
       "       1.37290498e-01, 2.59049964e-02, 1.13330046e-01, 5.49980217e-02,\n",
       "       5.95509885e-03, 1.14119063e-01, 2.62529052e-04, 2.15822262e-03,\n",
       "       3.52819412e-03])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.31657086644717086, 'median_income'),\n",
       " (0.13729049814248565, 'income_cat'),\n",
       " (0.11411906349742813, 'INLAND'),\n",
       " (0.11333004610673754, 'population_per_household'),\n",
       " (0.05772133043079134, 'longitude'),\n",
       " (0.05766632152077914, 'latitude'),\n",
       " (0.05499802170277036, 'bedrooms_per_room'),\n",
       " (0.044266919879627505, 'housing_median_age'),\n",
       " (0.02590499643107812, 'rooms_per_household'),\n",
       " (0.017172677387062883, 'total_rooms'),\n",
       " (0.01689603938060975, 'population'),\n",
       " (0.016789960391464395, 'total_bedrooms'),\n",
       " (0.015369214049002431, 'households'),\n",
       " (0.005955098849464164, '<1H OCEAN'),\n",
       " (0.003528194116232283, 'NEAR OCEAN'),\n",
       " (0.002158222615104861, 'NEAR BAY'),\n",
       " (0.00026252905219063924, 'ISLAND')]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_attribs = [\"rooms_per_household\", \"population_per_household\", \"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances, attributes), reverse = True)  # 重要度排序"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过测试集评估系统\n",
    "    1.从测试集获取预测器和标签\n",
    "    2.运full_pipline来转换数据\n",
    "    3.在测试集上评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51141.318883697044"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "final_model\n",
    "\n",
    "X_test = strat_test_set.drop('median_house_value', axis=1) # 删除房价值\n",
    "y_test = strat_test_set['median_house_value'].copy()       # 取目标房价值\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "X_test_prepared\n",
    "\n",
    "# df=pd.DataFrame(X_test_prepared)\n",
    "# df\n",
    "\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目启动阶段\n",
    "    1.展示解决方案 学习了什么\n",
    "    2.什么有用\n",
    "    3.什么没用\n",
    "    4.基于什么假设\n",
    "    5.以及系统的限制有哪些\n",
    "    6.制作漂亮的演示文稿，例如收入中位数是预测房价的首选指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 启动，监控和维护系统\n",
    "    1.为生产环境做好准备，将生产数据源接入系统\n",
    "    2.编写监控代码，定期检查系统的实时性能，性能下降时触发警报，系统崩溃和性能退化\n",
    "    3.时间维护，模型会渐渐腐坏，定期使用新数据训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评估系统性能\n",
    "    1.需要对系统的预测结果进行抽样评估，需要人工分析，分析师可能是专家，平台工作人员，都需要将人工评估的流水线接入系统\n",
    "    2.评估输入系统的数据质量\n",
    "    3.使用新鲜数据定期训练你的模型，最多6个月"
   ]
  }
 ],
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